Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Syncnox MCP Server optimize route R100 for shortest distance"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server that gives AI agents (like Claude) direct access to the Syncnox route-management backend.
Streamable HTTP — the server exposes a single HTTP endpoint at /mcp.
This means you can deploy it in Docker, expose it over the network, and connect multiple remote clients.
Related MCP server: Logistics AI MCP
Every request must include the header:
X-API-Key: <your MCP_API_KEY>The server itself also authenticates to your Syncnox FastAPI backend using a Bearer token (API_KEY).
Tool | What it does |
| Add a new stop to an existing route |
| Re-order stops for fastest time or shortest distance |
| Get ETA for the whole route or a single stop |
| Fetch full route details including all stops |
| Fetch the details of a specific job by its ID |
| Add a single job (address geocoded automatically) |
| Add multiple jobs from a JSON list of dictionaries (addresses geocoded automatically) |
| Import jobs from CSV content passed directly as text |
| Import a list of jobs from a local CSV/Excel file path |
syncnox-mcp/
├── server.py ← entry point, Streamable HTTP app
├── middleware.py ← API-key auth middleware (production ASGI wrapper)
├── config.py ← settings loaded from .env
├── api_client.py ← shared httpx client for Syncnox backend
├── tools/
│ ├── __init__.py ← central tools registry exposing register_all()
│ ├── routes.py ← route-related tools (get_route, optimize_route, get_eta)
│ ├── stops.py ← stop-related tools (add_stop)
│ └── jobs.py ← job-related tools (get_job_info)
├── requirements.txt
└── .env ← secrets (not committed to git)# 1. Create & activate virtual env
python -m venv .venv
source .venv/bin/activate
# 2. Install dependencies
pip install -r requirements.txt
# 3. Configure
cp .env .env.local # edit values
# Set: API_BASE_URL, API_KEY, MCP_API_KEY, PORT
# 4. Run
python server.pyServer starts at http://0.0.0.0:8100.
In Claude Desktop (claude_desktop_config.json):
{
"mcpServers": {
"syncnox": {
"type": "http",
"url": "http://localhost:8100/mcp",
"headers": {
"X-API-Key": "changeme-mcp-secret"
}
}
}
}curl http://localhost:8100/health
# → OKcd syncnox-mcp
git pull
sudo systemctl restart syncnox-mcp.serviceThis server cannot be deployed
Agentic-AI route optimization for delivery fleets, capacity- and time-window-safe.
Route optimization for delivery fleets: plan, edit, and re-optimize multi-stop routes by prompt.
Geocoding, routing, isochrones, traffic, weather, and place search for AI agents. 19 MCP tools.
Travel tools for AI agents: plan and edit real trips, search stays and tours, import travel videos.
